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  4. Speaker-Independent Microphone Identification in Noisy Conditions
 
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2022
Conference Paper
Title

Speaker-Independent Microphone Identification in Noisy Conditions

Abstract
This work proposes a method for source device identification from speech recordings that applies neural-network-based denoising, to mitigate the impact of counter-forensics attacks using noise injection. The method is evaluated by comparing the impact of denoising on three state-of-the-art features for microphone classification, determining their discriminating power with and without denoising being applied. The proposed framework achieves a significant performance increase for noisy material, and more generally, validates the usefulness of applying denoising prior to device identification for noisy recordings.
Author(s)
Giganti, Antonio
Politecnico di Milano
Cuccovillo, Luca  
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Bestagini, Paolo
Politecnico di Milano  
Aichroth, Patrick  
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Tubaro, Stefano
Mainwork
30th European Signal Processing Conference, EUSIPCO 2022. Proceedings  
Conference
European Signal Processing Conference 2022  
Open Access
DOI
10.23919/EUSIPCO55093.2022.9909800
Additional full text version
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Language
English
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Keyword(s)
  • Audio Forensics

  • Source Attribution

  • Microphone Identification

  • Device Fingerprint

  • media forensics

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